Pre-stack modeling operator for multiple wavelets

Aperta
  • Qualcosa non va?
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Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
30/100
Tipo di issue
Bug
Chiarezza
Abbastanza chiara
Stato di attività
Ferma
Stack tecnologico
python
Ambito
backend

Direzione di ricerca

Start at the pre-stack inversion operator’s explicit=False path and read how it constructs Convolve1D operators and combines them with BlockDiag; compare this with the dense multi-wavelet path shown in the issue. First focus on the stated 1D case, leaving 2D and 3D extension for later. Done means sparse results for multiple wavelets are consistent with the dense implementation.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

enhancement

🛠 Multi-Wavelet Support in Pre-Stack Inversion Operator (explicit=False)

📖 Description

The convolution operator used by pylops for pre-stack inversion (explicit=False) is implemented using the Conv1D class. Currently, pylops supports only a single wavelet for all angles, meaning the inversion assumes the same wavelet for each angle.

I attempted to modify the code to allow a different wavelet for each angle (mid-angle in my case, as I’m using partial stacks). However, my current results are only consistent when using dense operators (explicit=True), whereas I would expect them to also work in the sparse case.

🔍 Observations

  • ✅ The dense implementation (explicit=True) behaves as expected.
  • ❌ The sparse implementation (explicit=False) does not produce comparable results.
  • ⚠️ The issue seems related to how the Conv1D operator is built when handling multiple wavelets.

The code using Conv1D for multiple wavelets is only working for spatdims=1 at the moment, the idea is to extend for 2D or 3D cases when i adress this issue.

💡 Code Snippet

✅ Dense Implementation (Working)
# Handling multiple wavelets
if isinstance(wav, list):
    print("Dense multi-wavelet operator")
    n = len(wav)
    C = np.zeros((n * nt0, n * nt0))

    wsub_list = []
    for w in wav:
        wsub = np.asarray(convmtx(w, nt0, len(w) // 2)[:nt0])
        wsub_list.append(wsub)

    C = get_block_diag(theta)(*wsub_list)

else:
    # Create wavelet operator
    C = ncp.asarray(convmtx(wav, nt0, len(wav) // 2)[:nt0])
    C = [C] * ntheta
    C = get_block_diag(theta)(*C)

# Combine operators
M = ncp.dot(C, ncp.dot(G, D))
Preop = MatrixMult(M, otherdims=spatdims, dtype=dtype)
❌ Sparse implementation (Not-working)
    else:
        if isinstance(wav, list):
            print("Dense multi-wavelet operator")
            # Criar uma lista de operadores de convolução
            Cop_list = []

            for w in wav:
                Cop = Convolve1D(
                    (nt0),
                    h=w,
                    offset=len(w) // 2,  # Define o centro da wavelet
                    axis=0,
                    dtype='float32',
                    method='fft'
                )
                Cop_list.append(Cop.tosparse())  # Adiciona à lista de operadores'
            

            Cop = BlockDiag(Cop_list)

        else:
            print("Sparse single wavelet operator")
            # Create wavelet operator
            Cop = Convolve1D(
                dims,
                h=wav,
                offset=len(wav) // 2,
                axis=0,
                dtype=dtype,
            )
Lingua principale
Python
Stelle
539
Fork
126
Merge medio
2g 17h
PR unite (30g)
21

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